Overview

Dataset statistics

Number of variables20
Number of observations9266614
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 GiB
Average record size in memory156.0 B

Variable types

Categorical5
Numeric14
Unsupported1

Alerts

MES is highly overall correlated with CLIENTEHigh correlation
CLIENTE is highly overall correlated with MES and 1 other fieldsHigh correlation
MORAS is highly overall correlated with M1 and 1 other fieldsHigh correlation
MARCACIONES is highly overall correlated with M1High correlation
M1 is highly overall correlated with MORAS and 2 other fieldsHigh correlation
M2 is highly overall correlated with MORAS and 2 other fieldsHigh correlation
M3 is highly overall correlated with M2 and 1 other fieldsHigh correlation
C3 is highly overall correlated with M3High correlation
ANIO is highly overall correlated with CLIENTEHigh correlation
INGRESO is highly skewed (γ1 = 23.7257702)Skewed
FECHANACIMIENTO is an unsupported type, check if it needs cleaning or further analysisUnsupported
INGRESO has 568032 (6.1%) zerosZeros
CONTACTOS has 4951354 (53.4%) zerosZeros
M1 has 3220844 (34.8%) zerosZeros
C1 has 6567714 (70.9%) zerosZeros
M2 has 4422040 (47.7%) zerosZeros
C2 has 7017791 (75.7%) zerosZeros
M3 has 5348889 (57.7%) zerosZeros
C3 has 7399318 (79.8%) zerosZeros
ANTIGUEDAD has 3987610 (43.0%) zerosZeros

Reproduction

Analysis started2023-06-12 22:05:34.328081
Analysis finished2023-06-12 22:25:33.388454
Duration19 minutes and 59.06 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

ANIO
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size141.4 MiB
2019
6822569 
2020
2444045 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters37066456
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2019 6822569
73.6%
2020 2444045
 
26.4%

Length

2023-06-12T16:25:34.321723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-12T16:25:35.016317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2019 6822569
73.6%
2020 2444045
 
26.4%

Most occurring characters

ValueCountFrequency (%)
2 11710659
31.6%
0 11710659
31.6%
1 6822569
18.4%
9 6822569
18.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37066456
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 11710659
31.6%
0 11710659
31.6%
1 6822569
18.4%
9 6822569
18.4%

Most occurring scripts

ValueCountFrequency (%)
Common 37066456
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 11710659
31.6%
0 11710659
31.6%
1 6822569
18.4%
9 6822569
18.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37066456
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 11710659
31.6%
0 11710659
31.6%
1 6822569
18.4%
9 6822569
18.4%

MES
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2257026
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size141.4 MiB
2023-06-12T16:25:35.422254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.6335378
Coefficient of variation (CV)0.58363498
Kurtosis-1.3163862
Mean6.2257026
Median Absolute Deviation (MAD)3
Skewness0.15284969
Sum57691183
Variance13.202597
MonotonicityNot monotonic
2023-06-12T16:25:35.824283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 1040183
11.2%
2 995957
10.7%
12 941402
10.2%
1 893419
9.6%
5 732618
7.9%
6 718674
7.8%
10 717661
7.7%
11 707744
7.6%
7 685663
7.4%
9 672976
7.3%
Other values (2) 1160317
12.5%
ValueCountFrequency (%)
1 893419
9.6%
2 995957
10.7%
3 1040183
11.2%
4 671853
7.3%
5 732618
7.9%
6 718674
7.8%
7 685663
7.4%
8 488464
5.3%
9 672976
7.3%
10 717661
7.7%
ValueCountFrequency (%)
12 941402
10.2%
11 707744
7.6%
10 717661
7.7%
9 672976
7.3%
8 488464
5.3%
7 685663
7.4%
6 718674
7.8%
5 732618
7.9%
4 671853
7.3%
3 1040183
11.2%

CLIENTE
Real number (ℝ)

Distinct9264976
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7674337 × 1012
Minimum2.0191121 × 108
Maximum2.0201282 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size141.4 MiB
2023-06-12T16:25:37.419052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.0191121 × 108
5-th percentile2.0194776 × 1011
Q12.0193247 × 1012
median2.0197516 × 1012
Q32.0205423 × 1012
95-th percentile2.0201115 × 1013
Maximum2.0201282 × 1013
Range2.020108 × 1013
Interquartile range (IQR)1.217568 × 109

Descriptive statistics

Standard deviation7.6535534 × 1012
Coefficient of variation (CV)1.3270293
Kurtosis-0.16758324
Mean5.7674337 × 1012
Median Absolute Deviation (MAD)5.3373361 × 108
Skewness1.3390664
Sum-1.89565 × 1018
Variance5.8576879 × 1025
MonotonicityNot monotonic
2023-06-12T16:25:38.076990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.019129672 × 10122
 
< 0.1%
2.019129053 × 10122
 
< 0.1%
2.019119449 × 10122
 
< 0.1%
2.019119069 × 10122
 
< 0.1%
2.020126497 × 10122
 
< 0.1%
2.019119168 × 10122
 
< 0.1%
2.020124741 × 10122
 
< 0.1%
2.019126578 × 10122
 
< 0.1%
2.019116939 × 10122
 
< 0.1%
2.019127053 × 10122
 
< 0.1%
Other values (9264966) 9266594
> 99.9%
ValueCountFrequency (%)
201911206 1
< 0.1%
201911230 1
< 0.1%
201911299 1
< 0.1%
201911324 1
< 0.1%
201911601 1
< 0.1%
201911752 1
< 0.1%
201911773 1
< 0.1%
201911797 1
< 0.1%
201911844 1
< 0.1%
201911999 1
< 0.1%
ValueCountFrequency (%)
2.020128221 × 10131
< 0.1%
2.020128221 × 10131
< 0.1%
2.020128221 × 10131
< 0.1%
2.02012822 × 10131
< 0.1%
2.02012822 × 10131
< 0.1%
2.02012822 × 10131
< 0.1%
2.02012822 × 10131
< 0.1%
2.02012822 × 10131
< 0.1%
2.02012822 × 10131
< 0.1%
2.02012822 × 10131
< 0.1%

ESTADO
Categorical

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size141.4 MiB
ESTADO DE MEXICO
1265732 
VERACRUZ
722681 
JALISCO
 
539577
CIUDAD DE MEXICO
 
473351
GUANAJUATO
 
471944
Other values (27)
5793329 

Length

Max length21
Median length16
Mean length10.208601
Min length6

Characters and Unicode

Total characters94599166
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJALISCO
2nd rowVERACRUZ
3rd rowCHIAPAS
4th rowTAMAULIPAS
5th rowESTADO DE MEXICO

Common Values

ValueCountFrequency (%)
ESTADO DE MEXICO 1265732
 
13.7%
VERACRUZ 722681
 
7.8%
JALISCO 539577
 
5.8%
CIUDAD DE MEXICO 473351
 
5.1%
GUANAJUATO 471944
 
5.1%
SINALOA 462219
 
5.0%
PUEBLA 426476
 
4.6%
MICHOACAN 380609
 
4.1%
NUEVO LEON 347600
 
3.8%
BAJA CALIFORNIA NORTE 326549
 
3.5%
Other values (22) 3849876
41.5%

Length

2023-06-12T16:25:38.450076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mexico 1739083
 
12.0%
de 1739083
 
12.0%
estado 1265732
 
8.8%
veracruz 722681
 
5.0%
jalisco 539577
 
3.7%
ciudad 473351
 
3.3%
guanajuato 471944
 
3.3%
sinaloa 462219
 
3.2%
puebla 426476
 
3.0%
baja 419292
 
2.9%
Other values (30) 6193709
42.9%

Most occurring characters

ValueCountFrequency (%)
A 14738419
15.6%
O 9314345
 
9.8%
E 8286933
 
8.8%
C 6689542
 
7.1%
I 6491004
 
6.9%
5186533
 
5.5%
U 5149995
 
5.4%
S 4360280
 
4.6%
D 4304728
 
4.6%
R 4159905
 
4.4%
Other values (15) 25917482
27.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 89412633
94.5%
Space Separator 5186533
 
5.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 14738419
16.5%
O 9314345
10.4%
E 8286933
 
9.3%
C 6689542
 
7.5%
I 6491004
 
7.3%
U 5149995
 
5.8%
S 4360280
 
4.9%
D 4304728
 
4.8%
R 4159905
 
4.7%
N 4030168
 
4.5%
Other values (14) 21887314
24.5%
Space Separator
ValueCountFrequency (%)
5186533
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 89412633
94.5%
Common 5186533
 
5.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 14738419
16.5%
O 9314345
10.4%
E 8286933
 
9.3%
C 6689542
 
7.5%
I 6491004
 
7.3%
U 5149995
 
5.8%
S 4360280
 
4.9%
D 4304728
 
4.8%
R 4159905
 
4.7%
N 4030168
 
4.5%
Other values (14) 21887314
24.5%
Common
ValueCountFrequency (%)
5186533
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94599166
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 14738419
15.6%
O 9314345
 
9.8%
E 8286933
 
8.8%
C 6689542
 
7.1%
I 6491004
 
6.9%
5186533
 
5.5%
U 5149995
 
5.4%
S 4360280
 
4.6%
D 4304728
 
4.6%
R 4159905
 
4.4%
Other values (15) 25917482
27.4%

INGRESO
Real number (ℝ)

SKEWED  ZEROS 

Distinct15986
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9092.36
Minimum0
Maximum2000013
Zeros568032
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size141.4 MiB
2023-06-12T16:25:38.981681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16000
median8000
Q311000
95-th percentile20000
Maximum2000013
Range2000013
Interquartile range (IQR)5000

Descriptive statistics

Standard deviation7474.5292
Coefficient of variation (CV)0.82206702
Kurtosis4099.4655
Mean9092.36
Median Absolute Deviation (MAD)2100
Skewness23.72577
Sum8.425539 × 1010
Variance55868587
MonotonicityNot monotonic
2023-06-12T16:25:39.553200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8000 1090779
 
11.8%
6000 897621
 
9.7%
10000 782103
 
8.4%
12000 645310
 
7.0%
0 568032
 
6.1%
5000 440763
 
4.8%
7000 422493
 
4.6%
15000 381479
 
4.1%
9000 342057
 
3.7%
4000 332174
 
3.6%
Other values (15976) 3363803
36.3%
ValueCountFrequency (%)
0 568032
6.1%
0.01 44
 
< 0.1%
0.05 5
 
< 0.1%
0.08 8
 
< 0.1%
0.1 20
 
< 0.1%
0.3 9
 
< 0.1%
0.5 19
 
< 0.1%
1 5396
 
0.1%
2 334
 
< 0.1%
3 274
 
< 0.1%
ValueCountFrequency (%)
2000013 6
 
< 0.1%
1000028 10
 
< 0.1%
1000000 12
 
< 0.1%
320003 3
 
< 0.1%
250000 346
< 0.1%
249750 2
 
< 0.1%
245000 1
 
< 0.1%
240000 45
 
< 0.1%
230000 10
 
< 0.1%
227000 7
 
< 0.1%

MORAS
Real number (ℝ)

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6533287
Minimum1
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.0 MiB
2023-06-12T16:25:39.881831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile7
Maximum36
Range35
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0957714
Coefficient of variation (CV)0.78986496
Kurtosis3.004146
Mean2.6533287
Median Absolute Deviation (MAD)1
Skewness1.4176305
Sum24587373
Variance4.3922577
MonotonicityNot monotonic
2023-06-12T16:25:40.319134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1 4115519
44.4%
2 1674671
18.1%
3 937651
 
10.1%
4 727109
 
7.8%
5 603888
 
6.5%
6 509919
 
5.5%
7 371065
 
4.0%
8 282357
 
3.0%
9 24378
 
0.3%
10 9156
 
0.1%
Other values (22) 10901
 
0.1%
ValueCountFrequency (%)
1 4115519
44.4%
2 1674671
18.1%
3 937651
 
10.1%
4 727109
 
7.8%
5 603888
 
6.5%
6 509919
 
5.5%
7 371065
 
4.0%
8 282357
 
3.0%
9 24378
 
0.3%
10 9156
 
0.1%
ValueCountFrequency (%)
36 10
 
< 0.1%
34 32
 
< 0.1%
30 119
< 0.1%
29 83
< 0.1%
28 78
< 0.1%
27 81
< 0.1%
26 102
< 0.1%
25 106
< 0.1%
24 156
< 0.1%
23 185
< 0.1%

SEXO
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size141.4 MiB
F
5233816 
M
4032798 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9266614
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowF
4th rowM
5th rowF

Common Values

ValueCountFrequency (%)
F 5233816
56.5%
M 4032798
43.5%

Length

2023-06-12T16:25:40.672267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-12T16:25:40.960570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
f 5233816
56.5%
m 4032798
43.5%

Most occurring characters

ValueCountFrequency (%)
F 5233816
56.5%
M 4032798
43.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 9266614
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 5233816
56.5%
M 4032798
43.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 9266614
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 5233816
56.5%
M 4032798
43.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9266614
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 5233816
56.5%
M 4032798
43.5%

ESTADOCIVIL
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size141.4 MiB
C
4249606 
S
3101934 
U
1536087 
V
 
207191
D
 
171796

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9266614
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowC
3rd rowS
4th rowU
5th rowU

Common Values

ValueCountFrequency (%)
C 4249606
45.9%
S 3101934
33.5%
U 1536087
 
16.6%
V 207191
 
2.2%
D 171796
 
1.9%

Length

2023-06-12T16:25:41.472857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-12T16:25:42.461117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
c 4249606
45.9%
s 3101934
33.5%
u 1536087
 
16.6%
v 207191
 
2.2%
d 171796
 
1.9%

Most occurring characters

ValueCountFrequency (%)
C 4249606
45.9%
S 3101934
33.5%
U 1536087
 
16.6%
V 207191
 
2.2%
D 171796
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 9266614
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 4249606
45.9%
S 3101934
33.5%
U 1536087
 
16.6%
V 207191
 
2.2%
D 171796
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 9266614
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 4249606
45.9%
S 3101934
33.5%
U 1536087
 
16.6%
V 207191
 
2.2%
D 171796
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9266614
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 4249606
45.9%
S 3101934
33.5%
U 1536087
 
16.6%
V 207191
 
2.2%
D 171796
 
1.9%

FECHANACIMIENTO
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size141.4 MiB

MARCACIONES
Real number (ℝ)

Distinct1367
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.661626
Minimum1
Maximum2342
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size141.4 MiB
2023-06-12T16:25:43.484607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q17
median26
Q367
95-th percentile196
Maximum2342
Range2341
Interquartile range (IQR)60

Descriptive statistics

Standard deviation73.77483
Coefficient of variation (CV)1.400922
Kurtosis20.627822
Mean52.661626
Median Absolute Deviation (MAD)22
Skewness3.343748
Sum4.8799496 × 108
Variance5442.7256
MonotonicityNot monotonic
2023-06-12T16:25:44.245706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 486933
 
5.3%
2 415281
 
4.5%
3 359164
 
3.9%
4 333691
 
3.6%
6 287103
 
3.1%
5 282011
 
3.0%
7 237659
 
2.6%
8 217871
 
2.4%
9 175105
 
1.9%
10 164710
 
1.8%
Other values (1357) 6307086
68.1%
ValueCountFrequency (%)
1 486933
5.3%
2 415281
4.5%
3 359164
3.9%
4 333691
3.6%
5 282011
3.0%
6 287103
3.1%
7 237659
2.6%
8 217871
2.4%
9 175105
 
1.9%
10 164710
 
1.8%
ValueCountFrequency (%)
2342 1
< 0.1%
2332 1
< 0.1%
2276 1
< 0.1%
2260 1
< 0.1%
2174 1
< 0.1%
2060 1
< 0.1%
2000 1
< 0.1%
1980 1
< 0.1%
1972 1
< 0.1%
1952 1
< 0.1%

CONTACTOS
Real number (ℝ)

Distinct110
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5000805
Minimum0
Maximum128
Zeros4951354
Zeros (%)53.4%
Negative0
Negative (%)0.0%
Memory size141.4 MiB
2023-06-12T16:25:45.024751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile7
Maximum128
Range128
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.2906071
Coefficient of variation (CV)2.1936203
Kurtosis54.172978
Mean1.5000805
Median Absolute Deviation (MAD)0
Skewness5.6605685
Sum13900667
Variance10.828095
MonotonicityNot monotonic
2023-06-12T16:25:48.866216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4951354
53.4%
1 1853537
 
20.0%
2 897248
 
9.7%
3 480147
 
5.2%
4 288679
 
3.1%
5 185722
 
2.0%
6 130699
 
1.4%
7 93232
 
1.0%
8 70755
 
0.8%
9 53985
 
0.6%
Other values (100) 261256
 
2.8%
ValueCountFrequency (%)
0 4951354
53.4%
1 1853537
 
20.0%
2 897248
 
9.7%
3 480147
 
5.2%
4 288679
 
3.1%
5 185722
 
2.0%
6 130699
 
1.4%
7 93232
 
1.0%
8 70755
 
0.8%
9 53985
 
0.6%
ValueCountFrequency (%)
128 1
 
< 0.1%
125 1
 
< 0.1%
113 1
 
< 0.1%
111 1
 
< 0.1%
109 3
< 0.1%
108 2
< 0.1%
106 1
 
< 0.1%
104 2
< 0.1%
102 1
 
< 0.1%
101 1
 
< 0.1%

M1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1363
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.511071
Minimum0
Maximum2342
Zeros3220844
Zeros (%)34.8%
Negative0
Negative (%)0.0%
Memory size141.4 MiB
2023-06-12T16:25:49.110451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q343
95-th percentile177
Maximum2342
Range2342
Interquartile range (IQR)43

Descriptive statistics

Standard deviation70.734484
Coefficient of variation (CV)1.8856962
Kurtosis25.338319
Mean37.511071
Median Absolute Deviation (MAD)6
Skewness3.8226151
Sum3.4760062 × 108
Variance5003.3672
MonotonicityNot monotonic
2023-06-12T16:25:49.350579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3220844
34.8%
1 366154
 
4.0%
2 302764
 
3.3%
3 231958
 
2.5%
4 205427
 
2.2%
6 173065
 
1.9%
5 170345
 
1.8%
7 131931
 
1.4%
8 120672
 
1.3%
9 97503
 
1.1%
Other values (1353) 4245951
45.8%
ValueCountFrequency (%)
0 3220844
34.8%
1 366154
 
4.0%
2 302764
 
3.3%
3 231958
 
2.5%
4 205427
 
2.2%
5 170345
 
1.8%
6 173065
 
1.9%
7 131931
 
1.4%
8 120672
 
1.3%
9 97503
 
1.1%
ValueCountFrequency (%)
2342 1
< 0.1%
2332 1
< 0.1%
2276 1
< 0.1%
2260 1
< 0.1%
2174 1
< 0.1%
2060 1
< 0.1%
2000 1
< 0.1%
1980 1
< 0.1%
1972 1
< 0.1%
1952 1
< 0.1%

C1
Real number (ℝ)

Distinct110
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0054215
Minimum0
Maximum128
Zeros6567714
Zeros (%)70.9%
Negative0
Negative (%)0.0%
Memory size141.4 MiB
2023-06-12T16:25:49.584564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile5
Maximum128
Range128
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.9322511
Coefficient of variation (CV)2.9164396
Kurtosis77.368529
Mean1.0054215
Median Absolute Deviation (MAD)0
Skewness6.8698641
Sum9316853
Variance8.5980965
MonotonicityNot monotonic
2023-06-12T16:25:49.902176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6567714
70.9%
1 1114772
 
12.0%
2 549689
 
5.9%
3 302234
 
3.3%
4 185388
 
2.0%
5 120613
 
1.3%
6 86648
 
0.9%
7 62455
 
0.7%
8 48367
 
0.5%
9 37308
 
0.4%
Other values (100) 191426
 
2.1%
ValueCountFrequency (%)
0 6567714
70.9%
1 1114772
 
12.0%
2 549689
 
5.9%
3 302234
 
3.3%
4 185388
 
2.0%
5 120613
 
1.3%
6 86648
 
0.9%
7 62455
 
0.7%
8 48367
 
0.5%
9 37308
 
0.4%
ValueCountFrequency (%)
128 1
 
< 0.1%
125 1
 
< 0.1%
113 1
 
< 0.1%
111 1
 
< 0.1%
109 3
< 0.1%
108 2
< 0.1%
106 1
 
< 0.1%
104 2
< 0.1%
102 1
 
< 0.1%
101 1
 
< 0.1%

M2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1332
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.301706
Minimum0
Maximum2342
Zeros4422040
Zeros (%)47.7%
Negative0
Negative (%)0.0%
Memory size141.4 MiB
2023-06-12T16:25:50.145424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q326
95-th percentile145
Maximum2342
Range2342
Interquartile range (IQR)26

Descriptive statistics

Standard deviation62.462259
Coefficient of variation (CV)2.2878519
Kurtosis37.043018
Mean27.301706
Median Absolute Deviation (MAD)1
Skewness4.6600524
Sum2.5299437 × 108
Variance3901.5338
MonotonicityNot monotonic
2023-06-12T16:25:50.382011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4422040
47.7%
1 382825
 
4.1%
2 295313
 
3.2%
3 214534
 
2.3%
4 183463
 
2.0%
5 144823
 
1.6%
6 141747
 
1.5%
7 107493
 
1.2%
8 99706
 
1.1%
9 80027
 
0.9%
Other values (1322) 3194643
34.5%
ValueCountFrequency (%)
0 4422040
47.7%
1 382825
 
4.1%
2 295313
 
3.2%
3 214534
 
2.3%
4 183463
 
2.0%
5 144823
 
1.6%
6 141747
 
1.5%
7 107493
 
1.2%
8 99706
 
1.1%
9 80027
 
0.9%
ValueCountFrequency (%)
2342 1
< 0.1%
2332 1
< 0.1%
2260 1
< 0.1%
2174 1
< 0.1%
2060 1
< 0.1%
2000 1
< 0.1%
1980 1
< 0.1%
1972 1
< 0.1%
1952 1
< 0.1%
1948 1
< 0.1%

C2
Real number (ℝ)

Distinct106
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.78674929
Minimum0
Maximum125
Zeros7017791
Zeros (%)75.7%
Negative0
Negative (%)0.0%
Memory size141.4 MiB
2023-06-12T16:25:50.618106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum125
Range125
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.5551473
Coefficient of variation (CV)3.2477275
Kurtosis101.0842
Mean0.78674929
Median Absolute Deviation (MAD)0
Skewness7.8037527
Sum7290502
Variance6.5287777
MonotonicityNot monotonic
2023-06-12T16:25:50.868833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7017791
75.7%
1 988036
 
10.7%
2 458176
 
4.9%
3 242414
 
2.6%
4 146223
 
1.6%
5 93983
 
1.0%
6 66656
 
0.7%
7 47380
 
0.5%
8 36660
 
0.4%
9 27970
 
0.3%
Other values (96) 141325
 
1.5%
ValueCountFrequency (%)
0 7017791
75.7%
1 988036
 
10.7%
2 458176
 
4.9%
3 242414
 
2.6%
4 146223
 
1.6%
5 93983
 
1.0%
6 66656
 
0.7%
7 47380
 
0.5%
8 36660
 
0.4%
9 27970
 
0.3%
ValueCountFrequency (%)
125 1
< 0.1%
113 1
< 0.1%
111 1
< 0.1%
109 1
< 0.1%
108 2
< 0.1%
104 2
< 0.1%
101 1
< 0.1%
100 1
< 0.1%
99 2
< 0.1%
97 1
< 0.1%

M3
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1301
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.300339
Minimum0
Maximum2342
Zeros5348889
Zeros (%)57.7%
Negative0
Negative (%)0.0%
Memory size141.4 MiB
2023-06-12T16:25:51.198649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312
95-th percentile118
Maximum2342
Range2342
Interquartile range (IQR)12

Descriptive statistics

Standard deviation54.476628
Coefficient of variation (CV)2.6835329
Kurtosis53.183284
Mean20.300339
Median Absolute Deviation (MAD)0
Skewness5.5460499
Sum1.8811541 × 108
Variance2967.703
MonotonicityNot monotonic
2023-06-12T16:25:51.439436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5348889
57.7%
1 326533
 
3.5%
2 257275
 
2.8%
3 187083
 
2.0%
4 161087
 
1.7%
6 127865
 
1.4%
5 127376
 
1.4%
7 96754
 
1.0%
8 88803
 
1.0%
9 70367
 
0.8%
Other values (1291) 2474582
26.7%
ValueCountFrequency (%)
0 5348889
57.7%
1 326533
 
3.5%
2 257275
 
2.8%
3 187083
 
2.0%
4 161087
 
1.7%
5 127376
 
1.4%
6 127865
 
1.4%
7 96754
 
1.0%
8 88803
 
1.0%
9 70367
 
0.8%
ValueCountFrequency (%)
2342 1
< 0.1%
2332 1
< 0.1%
2260 1
< 0.1%
2174 1
< 0.1%
2000 1
< 0.1%
1980 1
< 0.1%
1972 1
< 0.1%
1952 1
< 0.1%
1948 1
< 0.1%
1942 1
< 0.1%

C3
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct105
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.63660934
Minimum0
Maximum125
Zeros7399318
Zeros (%)79.8%
Negative0
Negative (%)0.0%
Memory size141.4 MiB
2023-06-12T16:25:51.716600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum125
Range125
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.2914719
Coefficient of variation (CV)3.5994947
Kurtosis128.96116
Mean0.63660934
Median Absolute Deviation (MAD)0
Skewness8.7540826
Sum5899213
Variance5.2508435
MonotonicityNot monotonic
2023-06-12T16:25:51.985291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7399318
79.8%
1 836638
 
9.0%
2 381935
 
4.1%
3 199085
 
2.1%
4 119241
 
1.3%
5 76253
 
0.8%
6 53743
 
0.6%
7 38160
 
0.4%
8 29353
 
0.3%
9 22260
 
0.2%
Other values (95) 110628
 
1.2%
ValueCountFrequency (%)
0 7399318
79.8%
1 836638
 
9.0%
2 381935
 
4.1%
3 199085
 
2.1%
4 119241
 
1.3%
5 76253
 
0.8%
6 53743
 
0.6%
7 38160
 
0.4%
8 29353
 
0.3%
9 22260
 
0.2%
ValueCountFrequency (%)
125 1
< 0.1%
113 1
< 0.1%
111 1
< 0.1%
109 1
< 0.1%
108 2
< 0.1%
106 1
< 0.1%
104 2
< 0.1%
100 1
< 0.1%
99 1
< 0.1%
97 1
< 0.1%

ANTIGUEDAD
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4666537
Minimum0
Maximum13
Zeros3987610
Zeros (%)43.0%
Negative0
Negative (%)0.0%
Memory size106.0 MiB
2023-06-12T16:25:52.190426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile6
Maximum13
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.0557252
Coefficient of variation (CV)1.4016432
Kurtosis4.4885205
Mean1.4666537
Median Absolute Deviation (MAD)1
Skewness2.0403626
Sum13590914
Variance4.2260061
MonotonicityNot monotonic
2023-06-12T16:25:52.397893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 3987610
43.0%
1 2331595
25.2%
2 1067682
 
11.5%
3 658261
 
7.1%
4 405704
 
4.4%
5 267476
 
2.9%
6 183504
 
2.0%
7 113984
 
1.2%
8 85511
 
0.9%
9 84436
 
0.9%
Other values (4) 80851
 
0.9%
ValueCountFrequency (%)
0 3987610
43.0%
1 2331595
25.2%
2 1067682
 
11.5%
3 658261
 
7.1%
4 405704
 
4.4%
5 267476
 
2.9%
6 183504
 
2.0%
7 113984
 
1.2%
8 85511
 
0.9%
9 84436
 
0.9%
ValueCountFrequency (%)
13 372
 
< 0.1%
12 4999
 
0.1%
11 26428
 
0.3%
10 49052
 
0.5%
9 84436
 
0.9%
8 85511
 
0.9%
7 113984
 
1.2%
6 183504
2.0%
5 267476
2.9%
4 405704
4.4%

EDAD
Real number (ℝ)

Distinct78
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.050236
Minimum18
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.0 MiB
2023-06-12T16:25:52.644745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile22
Q129
median38
Q350
95-th percentile65
Maximum95
Range77
Interquartile range (IQR)21

Descriptive statistics

Standard deviation13.443758
Coefficient of variation (CV)0.33567239
Kurtosis-0.56137807
Mean40.050236
Median Absolute Deviation (MAD)10
Skewness0.49031125
Sum3.7113008 × 108
Variance180.73464
MonotonicityNot monotonic
2023-06-12T16:25:52.891582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 300134
 
3.2%
26 292116
 
3.2%
28 289847
 
3.1%
25 286100
 
3.1%
29 276458
 
3.0%
24 273458
 
3.0%
30 258741
 
2.8%
31 247693
 
2.7%
32 241343
 
2.6%
33 231829
 
2.5%
Other values (68) 6568895
70.9%
ValueCountFrequency (%)
18 33260
 
0.4%
19 101647
 
1.1%
20 130305
1.4%
21 153889
1.7%
22 181253
2.0%
23 229657
2.5%
24 273458
3.0%
25 286100
3.1%
26 292116
3.2%
27 300134
3.2%
ValueCountFrequency (%)
95 1
 
< 0.1%
94 4
 
< 0.1%
93 8
 
< 0.1%
92 6
 
< 0.1%
91 10
 
< 0.1%
90 8
 
< 0.1%
89 23
 
< 0.1%
88 53
 
< 0.1%
87 82
< 0.1%
86 145
< 0.1%

TARGET
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size141.4 MiB
0
6898694 
1
2367920 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9266614
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 6898694
74.4%
1 2367920
 
25.6%

Length

2023-06-12T16:25:53.141202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-12T16:25:53.326147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 6898694
74.4%
1 2367920
 
25.6%

Most occurring characters

ValueCountFrequency (%)
0 6898694
74.4%
1 2367920
 
25.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9266614
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6898694
74.4%
1 2367920
 
25.6%

Most occurring scripts

ValueCountFrequency (%)
Common 9266614
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6898694
74.4%
1 2367920
 
25.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9266614
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6898694
74.4%
1 2367920
 
25.6%

Interactions

2023-06-12T16:23:01.302675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:16:11.688598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:16:43.053292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:17:14.058934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:17:47.908500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:18:18.557102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:18:50.318506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:19:21.431772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:19:53.209250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:20:24.129975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:20:55.632483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:21:26.632211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:21:58.576504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:22:29.517216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:23:03.545257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:16:14.201040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:16:45.214479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:17:16.247392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:17:50.114986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:18:20.810706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:18:52.558627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:19:23.662259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:19:55.464487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:20:26.419504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:20:57.853966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:21:28.866702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:22:00.813996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:22:31.740699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:23:05.802866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:16:16.743735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:16:47.437623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:17:18.360732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:17:52.323460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:18:23.036562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:18:54.794642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:19:25.911275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:19:57.704981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:20:28.665999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:21:00.073444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:21:31.082744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:22:03.041480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:22:34.000205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:23:08.201629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:16:18.958463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:16:49.760124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:17:20.869405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:17:54.458882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:18:25.494202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:18:56.956912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:19:28.076718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:19:59.879432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:20:30.874816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:21:02.247103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:21:33.251190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:22:05.207110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:22:36.191399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:23:10.484150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:16:21.170422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:16:51.990339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:17:23.383080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:17:56.661392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:18:27.985863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:18:59.195404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:19:30.520016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:20:02.108918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:20:33.081288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:21:04.488597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:21:35.547720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:22:07.472450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:22:38.453096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:23:12.763563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:16:23.380199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:16:54.206511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:17:25.544167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:17:58.823835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:18:30.304408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:19:01.330477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:19:33.092730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:20:04.312558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:20:35.354804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:21:06.701483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:21:37.785212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:22:09.703414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:22:40.693589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:23:15.021030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:16:25.568504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:16:56.466618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:17:30.409534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:18:01.042313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:18:32.562832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:19:03.510931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:19:35.444299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:20:06.515028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:20:37.906504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:21:08.938077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:21:40.033710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:22:11.931901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:22:42.948809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:23:17.247514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:16:27.764780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:16:58.711444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:17:32.557967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:18:03.222746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:18:34.796162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:19:05.803097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:19:37.673784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:20:08.692478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:20:40.320113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:21:11.304655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:21:42.563570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:22:14.131366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:22:45.165214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:23:19.521030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:16:29.977531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:17:00.928319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:17:34.736086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:18:05.419487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:18:37.097124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:19:08.036587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:19:39.922628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:20:10.911552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:20:42.486421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:21:13.548151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:21:45.175311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:22:16.394875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:22:47.616849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:23:21.755720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:16:32.166701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:17:03.148261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:17:36.917301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:18:07.612947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:18:39.295588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:19:10.275458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:19:42.136104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:20:13.101818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:20:44.676881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:21:15.680572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:21:47.549893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:22:18.599346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:22:50.170551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:23:24.015227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:16:34.403891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:17:05.379374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:17:39.138529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:18:09.826601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:18:41.514115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:19:12.485932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:19:44.451648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:20:15.363329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:20:46.884707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:21:17.904055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:21:49.746166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:22:20.815416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:22:52.523259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:23:26.245519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:16:36.573916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:17:07.550821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:17:41.360487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:18:11.985039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:18:43.727113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:19:14.856389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:19:46.690140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:20:17.582809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:20:49.084020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:21:20.114528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:21:51.973650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:22:22.952840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:22:54.722447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:23:28.441982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:16:38.706402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:17:09.677248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:17:43.518574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:18:14.144480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:18:45.907567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:19:16.994813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:19:48.839572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:20:19.749055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:20:51.223444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:21:22.242313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:21:54.153103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:22:25.092266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:22:56.881951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:23:30.610558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:16:40.846822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:17:11.847693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:17:45.662002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:18:16.305919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:18:48.092022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:19:19.131237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:19:51.010785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:20:21.878474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:20:53.382215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:21:24.383741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:21:56.311454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:22:27.246702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T16:22:59.070140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-12T16:25:53.540197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
MESCLIENTEINGRESOMORASMARCACIONESCONTACTOSM1C1M2C2M3C3ANTIGUEDADEDADANIOESTADOSEXOESTADOCIVILTARGET
MES1.0000.5730.013-0.115-0.0430.0460.0250.0650.0890.0960.1080.0780.0060.0290.3370.0120.0040.0060.111
CLIENTE0.5731.0000.031-0.049-0.0110.0590.0270.0780.0720.1020.1200.114-0.127-0.0610.5100.0440.0100.0180.076
INGRESO0.0130.0311.000-0.022-0.0030.055-0.0020.0380.0040.0390.0040.035-0.0050.0840.0010.0050.0020.0010.001
MORAS-0.115-0.049-0.0221.0000.431-0.1410.5620.1340.5040.1430.4040.1400.002-0.0970.0480.0190.0140.0240.119
MARCACIONES-0.043-0.011-0.0030.4311.000-0.1170.5350.0990.3460.0770.2830.0950.003-0.0800.0290.0060.0020.0050.047
CONTACTOS0.0460.0590.055-0.141-0.1171.000-0.1080.440-0.0890.322-0.0490.265-0.0960.0720.0030.0180.0100.0040.211
M10.0250.027-0.0020.5620.535-0.1081.0000.3480.6090.2130.4320.1890.017-0.0670.0380.0050.0030.0050.053
C10.0650.0780.0380.1340.0990.4400.3481.0000.1660.4480.1270.351-0.0600.0180.0060.0160.0090.0030.139
M20.0890.0720.0040.5040.346-0.0890.6090.1661.0000.4590.6090.2760.025-0.0520.0560.0050.0030.0050.044
C20.0960.1020.0390.1430.0770.3220.2130.4480.4591.0000.2350.443-0.0500.0140.0090.0140.0070.0030.103
M30.1080.1200.0040.4040.283-0.0490.4320.1270.6090.2351.0000.5340.050-0.0360.0510.0040.0020.0030.033
C30.0780.1140.0350.1400.0950.2650.1890.3510.2760.4430.5341.000-0.0290.0130.0190.0120.0070.0020.086
ANTIGUEDAD0.006-0.127-0.0050.0020.003-0.0960.017-0.0600.025-0.0500.050-0.0291.0000.1070.0470.0430.0310.0510.046
EDAD0.029-0.0610.084-0.097-0.0800.072-0.0670.018-0.0520.014-0.0360.0130.1071.0000.0430.0350.0800.2390.091
ANIO0.3370.5100.0010.0480.0290.0030.0380.0060.0560.0090.0510.0190.0470.0431.0000.0340.0050.0080.090
ESTADO0.0120.0440.0050.0190.0060.0180.0050.0160.0050.0140.0040.0120.0430.0350.0341.0000.0340.0810.065
SEXO0.0040.0100.0020.0140.0020.0100.0030.0090.0030.0070.0020.0070.0310.0800.0050.0341.0000.0800.013
ESTADOCIVIL0.0060.0180.0010.0240.0050.0040.0050.0030.0050.0030.0030.0020.0510.2390.0080.0810.0801.0000.042
TARGET0.1110.0760.0010.1190.0470.2110.0530.1390.0440.1030.0330.0860.0460.0910.0900.0650.0130.0421.000

Missing values

2023-06-12T16:23:36.702880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-12T16:24:03.904885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ANIOMESCLIENTEESTADOINGRESOMORASSEXOESTADOCIVILFECHANACIMIENTOMARCACIONESCONTACTOSM1C1M2C2M3C3ANTIGUEDADEDADTARGET
0201952019539059618JALISCO15000.03MS1966-02-0134781900013001531
1201952019527392560VERACRUZ6000.06MC1987-07-0411307134203005310
220195201958823716CHIAPAS4000.01FS1980-05-071774100111311440390
320195201953294752TAMAULIPAS4000.05MU1989-08-30139017611591173111290
4201952019568646789ESTADO DE MEXICO5000.03FU1968-05-16895288113000510
5201952019563063824PUEBLA6000.06FS1985-07-16103010806202301330
6201952019539910671SINALOA12000.08MS1988-08-2715431604843301301
7201952019521175745SINALOA8000.01MC1975-10-22410040102431
8201952019567381970CHIAPAS4500.02MC1978-06-127674100000401
9201952019567848537YUCATAN15000.02MC1950-10-17736121100681
ANIOMESCLIENTEESTADOINGRESOMORASSEXOESTADOCIVILFECHANACIMIENTOMARCACIONESCONTACTOSM1C1M2C2M3C3ANTIGUEDADEDADTARGET
927021320194201942011347NUEVO LEON0.0000001FC1974-10-10114100000441
927021420194201946633818VERACRUZ5000.0000001FS1993-09-15110032000251
9270215201942019456273055QUERETARO32000.0000001MS1993-12-14210021510250
927021620194201947391029GUANAJUATO6300.0000001MC1986-04-21210041410331
92702172019420194660127SONORA9093.4773221MC1948-06-141000000010700
927021820194201942290471SONORA12000.0000001MC1984-01-2820183112424350
927021920194201947067504ESTADO DE MEXICO6000.0000001FU1989-02-072000142631300
9270220201942019446430265ESTADO DE MEXICO8000.0000001MS1971-11-16115111101471
927022120194201943231046TAMAULIPAS5600.0000001FC1968-04-241100418110511
9270222201942019410035630ESTADO DE MEXICO0.0000001MU1987-07-05101013926362310